Simulation in virtual reality: Robotic training and surgical applications

Document Type : Research Note

Authors

1 Department of Mechatronics Engineering, University of Baghdad, Iraq

2 Department of Mechanical Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, Turkey

Abstract

Two case studies are performed in this study; one with 4-dof robotic system, the other 6-dof industrial robot arm . Both robot arms are actually operated in Mechatronics Laboratory, Gaziantep University. Different motion trajectories are designed, and implemented for training, medical tasks and surgical operations base. Simulations are built by using VR Toolbox in Matlab. Virtual reality environment is achieved through Simulink with real time examples . The motions and trajectories necessary for training and surgical applications are directly seen. This enables the surgeons training with many benefits; greater control during tasks reduced training periods, possibility of error free tasks for example.

Keywords

References

References:
1. Fulcar, V.N. and Shivramwar, M.V. "Applications of Haptics technology in advance robotics", IEEE- 2010, pp. 273-277 (2010).
2. Staub, C., Can, S., Jensen, B., Knoll, A., and Kohlbecher, S. "Human computer interfaces for interaction with surgical tools in robotic surgery", The 4th IEEE RAS/EMBS Int. Conf. on Biomedical Rob. and Biomechatronics-Italy, 2012-IEEE, pp. 81-86 (2012).
3. Khor, W.S., Baker, B., Amin, K., Chan, A., Patel, K., and Wong, J. "Augmented and virtual reality in surgery-the digital surgical environment: applications, limitations and legal pitfalls", Annals of Translational Medicine, 4(23), p. 454 (2016).
4. Al-Mashhadany, Y.I. "Scara robot: Modeled, simulated, and virtual-reality verified", Communications in Comp. and Information Science, CCIS 330, Springer- Verlag, pp. 94-102 (2016).
5. Buckley, C.E., Nugent, E., Ryan, D., and Neary, P.C. "Virtual reality-A new era in surgical training", Chapter 7-INTECH, Book: Virtual Reality in Psychological, Medical and Pedagogical Applications, pp. 139- 166 (2012).
6. Nooshabadi, Z.S., Abdi, E., Farahmand, F., Narimani, R., and Chizari, M. "A Meshless method to simulate the interactions between a large soft tissue and a surgical grasper", Scientia Iranica, 23(1), pp. 295-300 (2016).
7. Almusawi, A.R.J., Dulger, L.C., and Kapucu, S. "Robotic arm dynamic and simulation with virtual reality model (VRM)", CoDIT'16-IEEE, Malta, pp. 335-340 (2016).
8. Almusawi, A.R.J. "Implementation of learning motion to control a robotic arm using haptic technology", PhD Thesis, Gaziantep University (2016).
9. Almusawi, A.R.J., Dulger, L.C., and Kapucu, S. "A new artificial neural network approach in solving inverse kinematics of robotic arm (Denso VP6242)", Computational Intelligence and Neuroscience, pp. 1-10 (2016).
10. Robotic Arm Edge OWI-535 Robot, Product Instruction Manual (2008).
11. Quanser.com. [assessed July 2018]. http://www. quanser.com/products/denso.
12. Leon, E.D., Nair, S.S., and Knoll, A. "User friendly Matlab-toolbox for symbolic robot dynamic modeling used for control design", IEEE-Int. Conf. on Robotics and Biomimetics, pp. 2181-2188 (2012).
13. Corke, P., Robotics, Vision and Control Fundamental Algorithms in MATLAB, V.73: Springer-Verlag Berlin-Heidelberg, pp. 191-205 (2011).
14. www.MathWorks, "Virtual Reality Modeling Language (VRML)-MATLAB/Simulink". 
Scientia Iranica
Volume 26, Issue 6 - Serial Number 6
Transactions on Mechanical Engineering (B)
November and December 2019
Pages 3369-3374

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